139,420 research outputs found
End-to-End Attention-based Large Vocabulary Speech Recognition
Many of the current state-of-the-art Large Vocabulary Continuous Speech
Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov
Models (HMMs). Most of these systems contain separate components that deal with
the acoustic modelling, language modelling and sequence decoding. We
investigate a more direct approach in which the HMM is replaced with a
Recurrent Neural Network (RNN) that performs sequence prediction directly at
the character level. Alignment between the input features and the desired
character sequence is learned automatically by an attention mechanism built
into the RNN. For each predicted character, the attention mechanism scans the
input sequence and chooses relevant frames. We propose two methods to speed up
this operation: limiting the scan to a subset of most promising frames and
pooling over time the information contained in neighboring frames, thereby
reducing source sequence length. Integrating an n-gram language model into the
decoding process yields recognition accuracies similar to other HMM-free
RNN-based approaches
Attention-Based End-to-End Speech Recognition on Voice Search
Recently, there has been a growing interest in end-to-end speech recognition
that directly transcribes speech to text without any predefined alignments. In
this paper, we explore the use of attention-based encoder-decoder model for
Mandarin speech recognition on a voice search task. Previous attempts have
shown that applying attention-based encoder-decoder to Mandarin speech
recognition was quite difficult due to the logographic orthography of Mandarin,
the large vocabulary and the conditional dependency of the attention model. In
this paper, we use character embedding to deal with the large vocabulary.
Several tricks are used for effective model training, including L2
regularization, Gaussian weight noise and frame skipping. We compare two
attention mechanisms and use attention smoothing to cover long context in the
attention model. Taken together, these tricks allow us to finally achieve a
character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on
the MiTV voice search dataset. While together with a trigram language model,
CER and SER reach 2.81% and 5.77%, respectively
E2E SPEECH RECOGNITION WITH CTC AND LOCAL ATTENTION
Many end-to-end, large vocabulary, continuous speech recognition systems are now able to achieve better speech recognition performance than conventional systems. Most of these approaches are based on bidirectional networks and sequence-to-sequence modeling however, so automatic speech recognition (ASR) systems using such techniques need to wait for an entire segment of voice input to be entered before they can begin processing the data, resulting in a lengthy time-lag, which can be a serious drawback in some applications. An obvious solution to this problem is to develop a speech recognition algorithm capable of processing streaming data. Therefore, in this paper we explore the possibility of a streaming, online, ASR system for Japanese using a model based on unidirectional LSTMs trained using connectionist temporal classification (CTC) criteria, with local attention. Such an approach has not been well investigated for use with Japanese, as most Japanese-language ASR systems employ bidirectional networks. The best result for our proposed system during experimental evaluation was a character error rate of 9.87%
Improved training of end-to-end attention models for speech recognition
Sequence-to-sequence attention-based models on subword units allow simple
open-vocabulary end-to-end speech recognition. In this work, we show that such
models can achieve competitive results on the Switchboard 300h and LibriSpeech
1000h tasks. In particular, we report the state-of-the-art word error rates
(WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets
of LibriSpeech. We introduce a new pretraining scheme by starting with a high
time reduction factor and lowering it during training, which is crucial both
for convergence and final performance. In some experiments, we also use an
auxiliary CTC loss function to help the convergence. In addition, we train long
short-term memory (LSTM) language models on subword units. By shallow fusion,
we report up to 27% relative improvements in WER over the attention baseline
without a language model.Comment: submitted to Interspeech 201
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